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jacscanomaly

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Find short anomalies in microlensing light curves.

Fit a single-lens model (PSPL, FSPL, parallax), scan its residual for short bumps and dips, and describe what is found: a host fit the anomalies do not pull, and each anomaly as bump / dip / caustic with its time and FWHM.

Documentation: https://jacscanomaly.readthedocs.io/en/latest/

Installation

pip install jacscanomaly

Example

One light curve is one Event; the steps are its methods and the results stay on it.

import numpy as np
from jacscanomaly import Event

time, flux, ferr = np.load("example/example_data.npy").T
ev = Event(time, flux, ferr, ra_deg=268.4, dec_deg=-29.2, ephemeris="roman.txt")

ev.fit("pspl")
pspl_scan = ev.scan()
if pspl_scan.detected:
    ev.fit("fspl", start="pspl")
    fspl_scan = ev.scan()
    ev.fit("parallax", start="pspl")
    parallax_scan = ev.scan()
    if fspl_scan.detected and parallax_scan.detected:
        ev.fit_atoms("pspl")
        ev.fit_atoms("fspl")
        ev.fit_atoms("parallax")

ev.summary()
ev.write_json("event.json")
ev.plot_result("pspl")

Example data

The example light curves are drawn from 2,371 simulated Roman light curves of planetary microlensing events (with parallax and orbital motion), made by the Roman Galactic Exoplanet Survey Project Infrastructure Team (RGES PIT), WG07 Survey Simulations and Pipeline Validation (Farzaneh Zohrabi, Matthew Penny, Macy Huston, Ali Crisp, et al.), assuming the Cassan exoplanet mass function.

Background

The residual scan over anomaly times and durations follows the systematic KMTNet AnomalyFinder search:

Zang, W., Jung, Y., Yee, J., et al. (2021). Systematic KMTNet Planetary Anomaly Search, Paper I: OGLE-2019-BLG-1053Lb, A Buried Terrestrial Planet. AJ, 162, 163. DOI: 10.3847/1538-3881/ac12d4

Citation

If you use jacscanomaly in academic work, please cite the software (citation.cff).

Development

git clone git@github.com:NunotaKansuke/jacscanomaly.git
cd jacscanomaly
pip install -e ".[dev]"
pytest
sphinx-build -W -b html docs docs/_build/html

This package was developed with the help of AI coding assistants: OpenAI ChatGPT / Codex and Anthropic Claude (Claude Code).

Metadata

Release files for jacscanomaly 1.0.4

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jacscanomaly-1.0.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 Details for jacscanomaly-1.0.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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jacscanomaly-1.0.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64 Details for jacscanomaly-1.0.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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jacscanomaly-1.0.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 Details for jacscanomaly-1.0.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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